Overview

Dataset statistics

Number of variables15
Number of observations55292
Missing cells3262
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 MiB
Average record size in memory113.0 B

Variable types

Categorical5
Numeric9
Boolean1

Alerts

User Comments Added has constant value "0" Constant
Video Title has a high cardinality: 223 distinct values High cardinality
External Video ID has a high cardinality: 223 distinct values High cardinality
Thumbnail link has a high cardinality: 223 distinct values High cardinality
Country Code has a high cardinality: 233 distinct values High cardinality
Video Length is highly correlated with Average View PercentageHigh correlation
Views is highly correlated with Video Likes AddedHigh correlation
Video Likes Added is highly correlated with ViewsHigh correlation
Average View Percentage is highly correlated with Video LengthHigh correlation
Views is highly correlated with Video Likes Added and 2 other fieldsHigh correlation
Video Likes Added is highly correlated with Views and 2 other fieldsHigh correlation
Video Dislikes Added is highly correlated with Views and 2 other fieldsHigh correlation
Video Likes Removed is highly correlated with Views and 2 other fieldsHigh correlation
User Subscriptions Added is highly correlated with User Subscriptions RemovedHigh correlation
User Subscriptions Removed is highly correlated with User Subscriptions AddedHigh correlation
Views is highly correlated with Video Likes AddedHigh correlation
Video Likes Added is highly correlated with ViewsHigh correlation
Is Subscribed is highly correlated with User Comments AddedHigh correlation
User Comments Added is highly correlated with Is SubscribedHigh correlation
Views is highly correlated with Video Likes Added and 2 other fieldsHigh correlation
Video Likes Added is highly correlated with Views and 4 other fieldsHigh correlation
Video Dislikes Added is highly correlated with Views and 2 other fieldsHigh correlation
Video Likes Removed is highly correlated with Views and 3 other fieldsHigh correlation
User Subscriptions Added is highly correlated with Video Likes Added and 2 other fieldsHigh correlation
User Subscriptions Removed is highly correlated with Video Likes Added and 1 other fieldsHigh correlation
Average View Percentage has 1438 (2.6%) missing values Missing
Average Watch Time has 1438 (2.6%) missing values Missing
Views is highly skewed (γ1 = 119.5300004) Skewed
Video Likes Added is highly skewed (γ1 = 99.02482423) Skewed
Video Dislikes Added is highly skewed (γ1 = 105.6523379) Skewed
Video Likes Removed is highly skewed (γ1 = 83.20521254) Skewed
User Subscriptions Added is highly skewed (γ1 = 117.6209316) Skewed
User Subscriptions Removed is highly skewed (γ1 = 84.02905204) Skewed
Views has 1438 (2.6%) zeros Zeros
Video Likes Added has 34576 (62.5%) zeros Zeros
Video Dislikes Added has 53135 (96.1%) zeros Zeros
Video Likes Removed has 52313 (94.6%) zeros Zeros
User Subscriptions Added has 46830 (84.7%) zeros Zeros
User Subscriptions Removed has 53796 (97.3%) zeros Zeros

Reproduction

Analysis started2022-04-04 00:45:30.667774
Analysis finished2022-04-04 00:45:49.243577
Duration18.58 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Video Title
Categorical

HIGH CARDINALITY

Distinct223
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size432.1 KiB
How I Would Learn Data Science (If I Had to Start Over)
 
428
The Best Free Data Science Courses Nobody is Talking About
 
405
3 Proven Data Science Projects for Beginners (Kaggle)
 
390
How I Learned Data Science
 
384
How I Would Learn Data Science in 2021 (What Has Changed?)
 
380
Other values (218)
53305 

Length

Max length100
Median length55
Mean length54.33408088
Min length21

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row🌶 Hot Topics in Tech: Data Science Explained #SHORTS
2nd row🌶 Hot Topics in Tech: Data Science Explained #SHORTS
3rd row🌶 Hot Topics in Tech: Data Science Explained #SHORTS
4th row🌶 Hot Topics in Tech: Data Science Explained #SHORTS
5th row🌶 Hot Topics in Tech: Data Science Explained #SHORTS

Common Values

ValueCountFrequency (%)
How I Would Learn Data Science (If I Had to Start Over)428
 
0.8%
The Best Free Data Science Courses Nobody is Talking About405
 
0.7%
3 Proven Data Science Projects for Beginners (Kaggle)390
 
0.7%
How I Learned Data Science384
 
0.7%
How I Would Learn Data Science in 2021 (What Has Changed?)380
 
0.7%
Why I Quit Data Science377
 
0.7%
Beginner Kaggle Data Science Project Walk-Through (Titanic)376
 
0.7%
Why You Probably Won't Become a Data Scientist375
 
0.7%
How I Would Learn Data Science in 2022 (If I Had to Start Over)373
 
0.7%
3 Reasons You Should NOT Become a Data Scientist369
 
0.7%
Other values (213)51435
93.0%

Length

2022-04-04T00:45:49.573065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
data49359
 
9.6%
science37383
 
7.2%
20756
 
4.0%
a12450
 
2.4%
to11723
 
2.3%
the11399
 
2.2%
how10378
 
2.0%
your7708
 
1.5%
i7393
 
1.4%
projects7174
 
1.4%
Other values (610)340682
66.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

External Video ID
Categorical

HIGH CARDINALITY

Distinct223
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size432.1 KiB
4OZip0cgOho
 
428
Ip50cXvpWY4
 
405
8igH8qZafpo
 
390
n3vw0M5RrPU
 
384
41Clrh6nv1s
 
380
Other values (218)
53305 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOtqQYqRNDGI
2nd rowOtqQYqRNDGI
3rd rowOtqQYqRNDGI
4th rowOtqQYqRNDGI
5th rowOtqQYqRNDGI

Common Values

ValueCountFrequency (%)
4OZip0cgOho428
 
0.8%
Ip50cXvpWY4405
 
0.7%
8igH8qZafpo390
 
0.7%
n3vw0M5RrPU384
 
0.7%
41Clrh6nv1s380
 
0.7%
SVtRsDhHlDk377
 
0.7%
I3FBJdiExcg376
 
0.7%
sHRq-LshG3U375
 
0.7%
xpIFS6jZbe8373
 
0.7%
m5pwx3hgtzM369
 
0.7%
Other values (213)51435
93.0%

Length

2022-04-04T00:45:49.770865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4ozip0cgoho428
 
0.8%
ip50cxvpwy4405
 
0.7%
8igh8qzafpo390
 
0.7%
n3vw0m5rrpu384
 
0.7%
41clrh6nv1s380
 
0.7%
svtrsdhhldk377
 
0.7%
i3fbjdiexcg376
 
0.7%
shrq-lshg3u375
 
0.7%
xpifs6jzbe8373
 
0.7%
m5pwx3hgtzm369
 
0.7%
Other values (213)51435
93.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Video Length
Real number (ℝ≥0)

HIGH CORRELATION

Distinct202
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean896.6780728
Minimum47
Maximum5029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size432.1 KiB
2022-04-04T00:45:49.956856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile60
Q1375
median545
Q3934
95-th percentile2968
Maximum5029
Range4982
Interquartile range (IQR)559

Descriptive statistics

Standard deviation927.3808005
Coefficient of variation (CV)1.034240525
Kurtosis4.531092553
Mean896.6780728
Median Absolute Deviation (MAD)214
Skewness2.169722905
Sum49579124
Variance860035.1492
MonotonicityNot monotonic
2022-04-04T00:45:50.180632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
591248
 
2.3%
467865
 
1.6%
375836
 
1.5%
495807
 
1.5%
549733
 
1.3%
291717
 
1.3%
392623
 
1.1%
334611
 
1.1%
378555
 
1.0%
355552
 
1.0%
Other values (192)47745
86.4%
ValueCountFrequency (%)
47230
 
0.4%
51133
 
0.2%
53196
 
0.4%
55166
 
0.3%
56491
 
0.9%
57191
 
0.3%
591248
2.3%
60265
 
0.5%
11478
 
0.1%
128244
 
0.4%
ValueCountFrequency (%)
5029183
0.3%
5005169
0.3%
4518212
0.4%
4119327
0.6%
4112297
0.5%
3735177
0.3%
3706207
0.4%
3659242
0.4%
3493207
0.4%
3413174
0.3%

Thumbnail link
Categorical

HIGH CARDINALITY

Distinct223
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size432.1 KiB
https://i.ytimg.com/vi/4OZip0cgOho/hqdefault.jpg
 
428
https://i.ytimg.com/vi/Ip50cXvpWY4/hqdefault.jpg
 
405
https://i.ytimg.com/vi/8igH8qZafpo/hqdefault.jpg
 
390
https://i.ytimg.com/vi/n3vw0M5RrPU/hqdefault.jpg
 
384
https://i.ytimg.com/vi/41Clrh6nv1s/hqdefault.jpg
 
380
Other values (218)
53305 

Length

Max length48
Median length48
Mean length48
Min length48

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttps://i.ytimg.com/vi/OtqQYqRNDGI/hqdefault.jpg
2nd rowhttps://i.ytimg.com/vi/OtqQYqRNDGI/hqdefault.jpg
3rd rowhttps://i.ytimg.com/vi/OtqQYqRNDGI/hqdefault.jpg
4th rowhttps://i.ytimg.com/vi/OtqQYqRNDGI/hqdefault.jpg
5th rowhttps://i.ytimg.com/vi/OtqQYqRNDGI/hqdefault.jpg

Common Values

ValueCountFrequency (%)
https://i.ytimg.com/vi/4OZip0cgOho/hqdefault.jpg428
 
0.8%
https://i.ytimg.com/vi/Ip50cXvpWY4/hqdefault.jpg405
 
0.7%
https://i.ytimg.com/vi/8igH8qZafpo/hqdefault.jpg390
 
0.7%
https://i.ytimg.com/vi/n3vw0M5RrPU/hqdefault.jpg384
 
0.7%
https://i.ytimg.com/vi/41Clrh6nv1s/hqdefault.jpg380
 
0.7%
https://i.ytimg.com/vi/SVtRsDhHlDk/hqdefault.jpg377
 
0.7%
https://i.ytimg.com/vi/I3FBJdiExcg/hqdefault.jpg376
 
0.7%
https://i.ytimg.com/vi/sHRq-LshG3U/hqdefault.jpg375
 
0.7%
https://i.ytimg.com/vi/xpIFS6jZbe8/hqdefault.jpg373
 
0.7%
https://i.ytimg.com/vi/m5pwx3hgtzM/hqdefault.jpg369
 
0.7%
Other values (213)51435
93.0%

Length

2022-04-04T00:45:50.371674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://i.ytimg.com/vi/4ozip0cgoho/hqdefault.jpg428
 
0.8%
https://i.ytimg.com/vi/ip50cxvpwy4/hqdefault.jpg405
 
0.7%
https://i.ytimg.com/vi/8igh8qzafpo/hqdefault.jpg390
 
0.7%
https://i.ytimg.com/vi/n3vw0m5rrpu/hqdefault.jpg384
 
0.7%
https://i.ytimg.com/vi/41clrh6nv1s/hqdefault.jpg380
 
0.7%
https://i.ytimg.com/vi/svtrsdhhldk/hqdefault.jpg377
 
0.7%
https://i.ytimg.com/vi/i3fbjdiexcg/hqdefault.jpg376
 
0.7%
https://i.ytimg.com/vi/shrq-lshg3u/hqdefault.jpg375
 
0.7%
https://i.ytimg.com/vi/xpifs6jzbe8/hqdefault.jpg373
 
0.7%
https://i.ytimg.com/vi/m5pwx3hgtzm/hqdefault.jpg369
 
0.7%
Other values (213)51435
93.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Country Code
Categorical

HIGH CARDINALITY

Distinct233
Distinct (%)0.4%
Missing386
Missing (%)0.7%
Memory size432.1 KiB
NL
 
445
AU
 
445
US
 
445
DE
 
445
GB
 
445
Other values (228)
52681 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowHK
2nd rowME
3rd rowRW
4th rowUS
5th rowDE

Common Values

ValueCountFrequency (%)
NL445
 
0.8%
AU445
 
0.8%
US445
 
0.8%
DE445
 
0.8%
GB445
 
0.8%
IN445
 
0.8%
MY444
 
0.8%
FR444
 
0.8%
VN444
 
0.8%
CA444
 
0.8%
Other values (223)50460
91.3%

Length

2022-04-04T00:45:50.540731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nl445
 
0.8%
us445
 
0.8%
de445
 
0.8%
gb445
 
0.8%
in445
 
0.8%
au445
 
0.8%
my444
 
0.8%
fr444
 
0.8%
vn444
 
0.8%
ca444
 
0.8%
Other values (223)50460
91.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Is Subscribed
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.1 KiB
False
28588 
True
26704 
ValueCountFrequency (%)
False28588
51.7%
True26704
48.3%
2022-04-04T00:45:50.655261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Views
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1558
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.5731571
Minimum0
Maximum285593
Zeros1438
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size432.1 KiB
2022-04-04T00:45:50.759351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median7
Q327
95-th percentile241.45
Maximum285593
Range285593
Interquartile range (IQR)25

Descriptive statistics

Standard deviation1704.966002
Coefficient of variation (CV)16.9524956
Kurtosis17956.63941
Mean100.5731571
Median Absolute Deviation (MAD)6
Skewness119.5300004
Sum5560891
Variance2906909.069
MonotonicityNot monotonic
2022-04-04T00:45:50.967541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19837
17.8%
25502
 
10.0%
33783
 
6.8%
42760
 
5.0%
52104
 
3.8%
61834
 
3.3%
71582
 
2.9%
01438
 
2.6%
81306
 
2.4%
91160
 
2.1%
Other values (1548)23986
43.4%
ValueCountFrequency (%)
01438
 
2.6%
19837
17.8%
25502
10.0%
33783
 
6.8%
42760
 
5.0%
52104
 
3.8%
61834
 
3.3%
71582
 
2.9%
81306
 
2.4%
91160
 
2.1%
ValueCountFrequency (%)
2855931
< 0.1%
2030551
< 0.1%
702401
< 0.1%
499821
< 0.1%
448351
< 0.1%
433141
< 0.1%
424221
< 0.1%
359501
< 0.1%
330981
< 0.1%
272631
< 0.1%

Video Likes Added
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct317
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.239311293
Minimum0
Maximum9165
Zeros34576
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size432.1 KiB
2022-04-04T00:45:51.178627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile11
Maximum9165
Range9165
Interquartile range (IQR)1

Descriptive statistics

Standard deviation65.29145094
Coefficient of variation (CV)15.40142878
Kurtosis12536.66906
Mean4.239311293
Median Absolute Deviation (MAD)0
Skewness99.02482423
Sum234400
Variance4262.973566
MonotonicityNot monotonic
2022-04-04T00:45:51.388496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034576
62.5%
18632
 
15.6%
23341
 
6.0%
31837
 
3.3%
41172
 
2.1%
5852
 
1.5%
6658
 
1.2%
7509
 
0.9%
8362
 
0.7%
9285
 
0.5%
Other values (307)3068
 
5.5%
ValueCountFrequency (%)
034576
62.5%
18632
 
15.6%
23341
 
6.0%
31837
 
3.3%
41172
 
2.1%
5852
 
1.5%
6658
 
1.2%
7509
 
0.9%
8362
 
0.7%
9285
 
0.5%
ValueCountFrequency (%)
91651
< 0.1%
84421
< 0.1%
43861
< 0.1%
26291
< 0.1%
19171
< 0.1%
17761
< 0.1%
17231
< 0.1%
16801
< 0.1%
15891
< 0.1%
14811
< 0.1%

Video Dislikes Added
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1060551255
Minimum0
Maximum399
Zeros53135
Zeros (%)96.1%
Negative0
Negative (%)0.0%
Memory size432.1 KiB
2022-04-04T00:45:51.597560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum399
Range399
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.507351484
Coefficient of variation (CV)23.64196423
Kurtosis14023.29063
Mean0.1060551255
Median Absolute Deviation (MAD)0
Skewness105.6523379
Sum5864
Variance6.286811466
MonotonicityNot monotonic
2022-04-04T00:45:51.807293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
053135
96.1%
11496
 
2.7%
2307
 
0.6%
3126
 
0.2%
450
 
0.1%
534
 
0.1%
627
 
< 0.1%
717
 
< 0.1%
1013
 
< 0.1%
812
 
< 0.1%
Other values (31)75
 
0.1%
ValueCountFrequency (%)
053135
96.1%
11496
 
2.7%
2307
 
0.6%
3126
 
0.2%
450
 
0.1%
534
 
0.1%
627
 
< 0.1%
717
 
< 0.1%
812
 
< 0.1%
99
 
< 0.1%
ValueCountFrequency (%)
3991
< 0.1%
2301
< 0.1%
1741
< 0.1%
1701
< 0.1%
1471
< 0.1%
1311
< 0.1%
541
< 0.1%
511
< 0.1%
391
< 0.1%
382
< 0.1%

Video Likes Removed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.175576937
Minimum0
Maximum436
Zeros52313
Zeros (%)94.6%
Negative0
Negative (%)0.0%
Memory size432.1 KiB
2022-04-04T00:45:52.027167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum436
Range436
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.601170733
Coefficient of variation (CV)20.51049981
Kurtosis8239.726658
Mean0.175576937
Median Absolute Deviation (MAD)0
Skewness83.20521254
Sum9708
Variance12.96843065
MonotonicityNot monotonic
2022-04-04T00:45:52.239216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
052313
94.6%
11891
 
3.4%
2485
 
0.9%
3185
 
0.3%
497
 
0.2%
564
 
0.1%
641
 
0.1%
734
 
0.1%
924
 
< 0.1%
823
 
< 0.1%
Other values (43)135
 
0.2%
ValueCountFrequency (%)
052313
94.6%
11891
 
3.4%
2485
 
0.9%
3185
 
0.3%
497
 
0.2%
564
 
0.1%
641
 
0.1%
734
 
0.1%
823
 
< 0.1%
924
 
< 0.1%
ValueCountFrequency (%)
4361
< 0.1%
3561
< 0.1%
3271
< 0.1%
3081
< 0.1%
2371
< 0.1%
1491
< 0.1%
1211
< 0.1%
1021
< 0.1%
771
< 0.1%
761
< 0.1%

User Subscriptions Added
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct239
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.365278883
Minimum0
Maximum9599
Zeros46830
Zeros (%)84.7%
Negative0
Negative (%)0.0%
Memory size432.1 KiB
2022-04-04T00:45:52.448122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum9599
Range9599
Interquartile range (IQR)0

Descriptive statistics

Standard deviation65.88953882
Coefficient of variation (CV)27.85698519
Kurtosis16418.41419
Mean2.365278883
Median Absolute Deviation (MAD)0
Skewness117.6209316
Sum130781
Variance4341.431326
MonotonicityNot monotonic
2022-04-04T00:45:52.664124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
046830
84.7%
13697
 
6.7%
21311
 
2.4%
3707
 
1.3%
4428
 
0.8%
5297
 
0.5%
6241
 
0.4%
7169
 
0.3%
8132
 
0.2%
9118
 
0.2%
Other values (229)1362
 
2.5%
ValueCountFrequency (%)
046830
84.7%
13697
 
6.7%
21311
 
2.4%
3707
 
1.3%
4428
 
0.8%
5297
 
0.5%
6241
 
0.4%
7169
 
0.3%
8132
 
0.2%
9118
 
0.2%
ValueCountFrequency (%)
95991
< 0.1%
95601
< 0.1%
37601
< 0.1%
26151
< 0.1%
17541
< 0.1%
15631
< 0.1%
15051
< 0.1%
14581
< 0.1%
12461
< 0.1%
11971
< 0.1%

User Subscriptions Removed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05338927874
Minimum0
Maximum103
Zeros53796
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size432.1 KiB
2022-04-04T00:45:52.854464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum103
Range103
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7669446756
Coefficient of variation (CV)14.36514397
Kurtosis10080.24578
Mean0.05338927874
Median Absolute Deviation (MAD)0
Skewness84.02905204
Sum2952
Variance0.5882041354
MonotonicityNot monotonic
2022-04-04T00:45:53.037968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
053796
97.3%
11048
 
1.9%
2210
 
0.4%
383
 
0.2%
461
 
0.1%
527
 
< 0.1%
623
 
< 0.1%
78
 
< 0.1%
95
 
< 0.1%
114
 
< 0.1%
Other values (15)27
 
< 0.1%
ValueCountFrequency (%)
053796
97.3%
11048
 
1.9%
2210
 
0.4%
383
 
0.2%
461
 
0.1%
527
 
< 0.1%
623
 
< 0.1%
78
 
< 0.1%
83
 
< 0.1%
95
 
< 0.1%
ValueCountFrequency (%)
1031
 
< 0.1%
931
 
< 0.1%
461
 
< 0.1%
221
 
< 0.1%
211
 
< 0.1%
191
 
< 0.1%
181
 
< 0.1%
171
 
< 0.1%
163
< 0.1%
151
 
< 0.1%

Average View Percentage
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct53339
Distinct (%)99.0%
Missing1438
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean0.3478206381
Minimum0
Maximum4.96779661
Zeros50
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size432.1 KiB
2022-04-04T00:45:53.390519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01261269551
Q10.1459483144
median0.3209825692
Q30.4861525058
95-th percentile0.8828756256
Maximum4.96779661
Range4.96779661
Interquartile range (IQR)0.3402041914

Descriptive statistics

Standard deviation0.2597338836
Coefficient of variation (CV)0.7467466136
Kurtosis5.793724931
Mean0.3478206381
Median Absolute Deviation (MAD)0.1702192838
Skewness1.238104983
Sum18731.53264
Variance0.0674616903
MonotonicityNot monotonic
2022-04-04T00:45:53.606762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1108
 
0.2%
050
 
0.1%
0.999660714310
 
< 0.1%
0.99310909098
 
< 0.1%
0.98298039227
 
< 0.1%
0.99696610177
 
< 0.1%
0.99955105116
 
< 0.1%
0.98315686276
 
< 0.1%
0.99943410856
 
< 0.1%
0.98567924536
 
< 0.1%
Other values (53329)53640
97.0%
(Missing)1438
 
2.6%
ValueCountFrequency (%)
050
0.1%
8.275261324 × 10-61
 
< 0.1%
2.433528616 × 10-51
 
< 0.1%
4.403669725 × 10-51
 
< 0.1%
6.88712763 × 10-51
 
< 0.1%
9.255178493 × 10-51
 
< 0.1%
9.718172983 × 10-51
 
< 0.1%
0.00011012916381
 
< 0.1%
0.00011312217191
 
< 0.1%
0.00012214069921
 
< 0.1%
ValueCountFrequency (%)
4.967796611
< 0.1%
4.6181734481
< 0.1%
3.5869677971
< 0.1%
3.33651
< 0.1%
2.9401234571
< 0.1%
2.897872341
< 0.1%
2.8271186441
< 0.1%
2.5986634771
< 0.1%
2.4969107141
< 0.1%
2.4654473681
< 0.1%

Average Watch Time
Real number (ℝ≥0)

MISSING

Distinct50508
Distinct (%)93.8%
Missing1438
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean205.2971559
Minimum0
Maximum5027.66
Zeros50
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size432.1 KiB
2022-04-04T00:45:53.809868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.50155
Q185.44289063
median170.139725
Q3259.4499583
95-th percentile520.835
Maximum5027.66
Range5027.66
Interquartile range (IQR)174.0070677

Descriptive statistics

Standard deviation206.5421061
Coefficient of variation (CV)1.006064137
Kurtosis53.25812607
Mean205.2971559
Median Absolute Deviation (MAD)87.09354368
Skewness4.991217213
Sum11056073.04
Variance42659.6416
MonotonicityNot monotonic
2022-04-04T00:45:54.040936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151
 
0.1%
050
 
0.1%
1233
 
0.1%
433
 
0.1%
232
 
0.1%
1132
 
0.1%
5932
 
0.1%
0.931
 
0.1%
10.931
 
0.1%
331
 
0.1%
Other values (50498)53498
96.8%
(Missing)1438
 
2.6%
ValueCountFrequency (%)
050
0.1%
0.0191
 
< 0.1%
0.0241
 
< 0.1%
0.0541
 
< 0.1%
0.0651
 
< 0.1%
0.17
 
< 0.1%
0.1091
 
< 0.1%
0.1131
 
< 0.1%
0.1471
 
< 0.1%
0.1621
 
< 0.1%
ValueCountFrequency (%)
5027.661
< 0.1%
4259.51
< 0.1%
4118.7211
< 0.1%
3878.5591
< 0.1%
3840.171
< 0.1%
3712.50251
< 0.1%
3708.9331
< 0.1%
3705.3271
< 0.1%
3492.4611
< 0.1%
3492.3121
< 0.1%

User Comments Added
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size432.1 KiB
0
55292 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
055292
100.0%

Length

2022-04-04T00:45:54.234242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-04T00:45:54.344370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
055292
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-04-04T00:45:46.369307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:33.292437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:35.000649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:36.573249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:38.112388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:39.908581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:41.455272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:43.026191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:44.794035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:46.539686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:33.622154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:35.176357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:36.743459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:38.290034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:40.079491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:41.628531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:43.197065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:44.963464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:46.712555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:33.792454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:35.350655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:36.912974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:38.465630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:40.250730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:41.804589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:43.365290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:45.141902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:46.881854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:33.958225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:35.522227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:37.082024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:38.827735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:40.419080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:41.974732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:43.535643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:45.315122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:47.071659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:34.140870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:35.707898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:37.261581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:39.016016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:40.599935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:42.162099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:43.729374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:45.500055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:47.242211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:34.306910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:35.880258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:37.426854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:39.192279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:40.765316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:42.330820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:44.087605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:45.671466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:47.415401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:34.476840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:36.056261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:37.597677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:39.370053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:40.936649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:42.503274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:44.275116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:45.848008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:47.588573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:34.647630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:36.230298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:37.769555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:39.549058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:41.111496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:42.677319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:44.452774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:46.022588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:47.761954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:34.826284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:36.401602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:37.942092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:39.729360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:41.283694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:42.850197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:44.622435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:45:46.199308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-04-04T00:45:54.403261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-04T00:45:54.642417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-04T00:45:54.879665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-04T00:45:55.112905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-04T00:45:55.269004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-04T00:45:48.060713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-04T00:45:48.500752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-04T00:45:48.900488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-04T00:45:49.035209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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